用弹性曲线规划与局部控制，让机器人精准摆放可变形长条物。
Deformable Linear Object Surface Placement Using Elastica Planning and Local Shape Control
- 基于欧拉弹性曲线设计高层规划，指定一端抓取+内部点对齐表面。
- 通过残差网络估计形状，低层反馈控制在误差下仍能完成任务。
- 适合食品包装等需高精度柔性物体操作的场景，实验用硅胶模拟物验证。
在受限环境中操控可变形线性物体（DLO）是一项挑战性任务。本文提出一种双层方法，使用单个机械手将DLO放置于平面表面。高层采用基于欧拉弹性曲线解的新颖DLO表面放置方法：机器人夹爪操控一个端点，同时选择DLO内部某点作为与目标表面对齐部分的起始点。低层构建了流水线控制器，利用残差神经网络（ResNet）估计DLO当前形状，并通过低层反馈确保在模型误差和放置偏差存在时仍能执行任务。该方法可在高层规划失败时实现恢复，符合实际机器人系统需求。通过仿真和实验验证，使用为生鲜食品应用准备的硅胶模拟物进行测试。
原文摘要 · Abstract (English)
Manipulation of deformable linear objects (DLOs) in constrained environments is a challenging task. This paper describes a two-layered approach for placing DLOs on a flat surface using a single robot hand. The high-level layer is a novel DLO surface placement method based on Euler's elastica solutions. During this process one DLO endpoint is manipulated by the robot gripper while a variable interior point of the DLO serves as the start point of the portion aligned with the placement surface. The low-level layer forms a pipeline controller. The controller estimates the DLO current shape using a Residual Neural Network (ResNet) and uses low-level feedback to ensure task execution in the presence of modeling and placement errors. The resulting DLO placement approach can recover from states where the high-level manipulation planner has failed as required by practical robot manipulation systems. The DLO placement approach is demonstrated with simulations and experiments that use silicon mock-up objects prepared for fresh food applications.
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